Postprocessing Optimization of RRT* Using Machine Learning and Information Theory for Robotic Navigation

Abstract

This work presents an alternative post-processing approach for optimizing mobile robot trajectories by combining vector quantization techniques with information theory. We developed an algorithm based on Vector Quantization (VQ) and Kullback-Leibler Divergence (VQKL) that maintains the original RRT*'s obstacle avoidance capabilities. When comparing VQKL and VQ with the Ramer-Douglas-Peucker (RDP) algorithm, our methods demonstrate significant superiority: VQ achieves a 13% reduction in path length (versus RDP's 10%) while VQKL achieves 14%, along with an 83% (VQ) and 84% (VQKL) reduction in node count compared to the original RRT* output. These results are obtained through an adaptive optimization process that iteratively adjusts centroids using a progressive annealing scheme. To ensure trajectory feasibility, we implemented a validation system that verifies both geometric deviation from the original path and collision-free operation with obstacles. Extensive simulations across 20 different environments with 100 trials each confirm that our method generates significantly shorter, more efficient, and safer trajectories, establishing a viable alternative for robotic path optimization.